{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import itertools \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.learning_curve import learning_curve\n",
    "from sklearn.learning_curve import validation_curve\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
       "0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29  5.64  1.04  3.92   \n",
       "1   1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "2   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "3   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "4   1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "\n",
       "     13  \n",
       "0  1065  \n",
       "1  1050  \n",
       "2  1185  \n",
       "3  1480  \n",
       "4   735  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", header = None)\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = \"\"\"Class,Alcohol,Malic acid,Ash,Alcalinity of ash,Magnesium,\n",
    "Total phenols,Flavanoids,Nonflavanoid phenols,Proanthocyanins,\n",
    "Color intensity,Hue,OD280/OD315 of diluted wines,Proline\"\"\".replace(\"\\n\", \"\").split(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0      1    14.23        1.71  2.43               15.6        127   \n",
       "1      1    13.20        1.78  2.14               11.2        100   \n",
       "2      1    13.16        2.36  2.67               18.6        101   \n",
       "3      1    14.37        1.95  2.50               16.8        113   \n",
       "4      1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.columns = names\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 14 columns):\n",
      "Class                           178 non-null int64\n",
      "Alcohol                         178 non-null float64\n",
      "Malic acid                      178 non-null float64\n",
      "Ash                             178 non-null float64\n",
      "Alcalinity of ash               178 non-null float64\n",
      "Magnesium                       178 non-null int64\n",
      "Total phenols                   178 non-null float64\n",
      "Flavanoids                      178 non-null float64\n",
      "Nonflavanoid phenols            178 non-null float64\n",
      "Proanthocyanins                 178 non-null float64\n",
      "Color intensity                 178 non-null float64\n",
      "Hue                             178 non-null float64\n",
      "OD280/OD315 of diluted wines    178 non-null float64\n",
      "Proline                         178 non-null int64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 19.5 KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = wine.iloc[:, 1:].values, wine.iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X dimension: (124, 13), Test X dimension: (54, 13)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)\n",
    "print(\"Train X dimension: %s, Test X dimension: %s\" % (X_train.shape, X_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline = Pipeline(\n",
    "    [(\"scl\", StandardScaler()),\n",
    "     (\"pca\", PCA(n_components = 2)),\n",
    "     (\"clf\", LogisticRegression(random_state = 0, penalty = \"l2\"))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scl', StandardScaler(copy=True, with_mean=True, with_std=True)), ('pca', PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)), ('clf', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98148148148148151"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(y = y_train, n_folds = 10, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy scores:  [ 0.92307692  0.92307692  0.92307692  1.          1.          1.\n",
      "  0.91666667  1.          1.          1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator = pipeline, X = X_train, y = y_train, cv = 10, n_jobs = -1)\n",
    "# n_jobs = -1: use all CPU cores for parallel computing\n",
    "\n",
    "print(\"CV accuracy scores: \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean CV accuracy: 0.969, std: 0.039\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean CV accuracy: %.3f, std: %.3f\" % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10fab3208>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LgJNmdo27373QgNrba2cXci2NBSo3nv/2M7N//vFHb2HtMp0YWkvPTy2NBSo3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\nSJKkZgwikiSpGYOIJElq5id159zm/RQ9YgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc2ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scores)\n",
    "plt.ylim(0.9, 1.01)\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Accuracy Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve(estimator = pipeline, X = X_train, y = y_train,\n",
    "                                                        train_sizes = np.linspace(0.1, 1.0, 10),\n",
    "                                                        cv = 10, \n",
    "                                                        n_jobs = -1)\n",
    "\n",
    "# learning_curve internally uses stratified_kfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "test_mean = np.mean(test_scores, axis = 1)\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "test_std = np.std(test_scores, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11014c6a0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/NcNvOfygXnvkddf93IBBoiS6ZkX43/vjv6PnF55TZKI194+VxgcGblEUq2RC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\nS5KyOu/VaSPXmZyo7ory89QtO6XBjs96b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eee+3DFFb/krrtuZ+bM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\nJvr/lmk5U/Y/+pkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110115748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.plot(train_sizes, train_mean, color = \"b\", marker = \"o\", markersize = 5, label = \"training accuracy scores\")\n",
    "plt.fill_between(train_sizes, train_mean + train_std, train_mean - train_std, alpha = 0.4)\n",
    "plt.plot(train_sizes, test_mean, color = \"g\", ls = \"--\", marker = \"s\", markersize = 5, label = \"validation accurancy\")\n",
    "plt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, alpha = 0.15, color = \"g\")\n",
    "\n",
    "\n",
    "plt.legend(loc = \"lower right\")\n",
    "plt.xlabel(\"Number of training samples\")\n",
    "plt.ylabel(\"Accuracy scores\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "param_range [    0.001     0.01      0.1       1.       10.      100.     1000.   ]\n"
     ]
    }
   ],
   "source": [
    "np.set_printoptions(precision = 4, suppress= True)\n",
    "param_range = 10.0 ** np.arange(-3, 4)\n",
    "print(\"param_range\", param_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scores, test_scores = validation_curve(estimator = pipeline, \n",
    "                                             X = X_train, \n",
    "                                             y = y_train, \n",
    "                                             param_name = \"clf__C\", \n",
    "                                             param_range = param_range,\n",
    "                                             cv = 10)\n",
    "\n",
    "# validation_curve internally uses stratified_kfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8, 1.02)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L6SIykyA26YjgDQzeiIiIiIiIiIj2GMO4faYI3pazZSS6i9ik0DYD4Bi8ERERERERERFN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\nkqRK8v8BX4dwZb4U7noAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114dea898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "test_mean = np.mean(test_scores, axis = 1)\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "test_std = np.std(test_scores, axis = 1)\n",
    "\n",
    "plt.figure(figsize = (15, 5))\n",
    "plt.plot(param_range, train_mean, color = \"b\", marker = \"o\", markersize = 5, label = \"training accuracy scores\")\n",
    "plt.fill_between(param_range, train_mean + train_std, train_mean - train_std, alpha = 0.4)\n",
    "plt.plot(param_range, test_mean, color = \"g\", ls = \"--\", marker = \"s\", markersize = 5, label = \"validation accurancy\")\n",
    "plt.fill_between(param_range, test_mean + test_std, test_mean - test_std, alpha = 0.15, color = \"g\")\n",
    "\n",
    "\n",
    "plt.legend(loc = \"lower right\")\n",
    "plt.xlabel(\"Complexity Parameter - C\")\n",
    "plt.ylabel(\"Accuracy scores\")\n",
    "plt.xscale(\"log\")\n",
    "plt.ylim(0.8, 1.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper Parameter Tuning using Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipe_svc = Pipeline([\n",
    "        (\"scl\", StandardScaler()),\n",
    "        (\"clf\", SVC(random_state = 0))\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0.0001,     0.001 ,     0.01  ,     0.1   ,     1.    ,\n",
       "          10.    ,   100.    ,  1000.    ])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_range = 10.0 ** np.arange(-4, 4)\n",
    "param_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {\"clf__C\": param_range, \"clf__kernel\": [\"linear\"]},\n",
    "    {\"clf__C\": param_range, \"clf__gamma\": param_range, \"clf__kernel\": [\"rbf\"]}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gs = GridSearchCV(estimator = pipe_svc, \n",
    "                 param_grid = param_grid,\n",
    "                 scoring = \"accuracy\",\n",
    "                 cv = 10, \n",
    "                 n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('scl', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False))]),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid=[{'clf__C': array([    0.0001,     0.001 ,     0.01  ,     0.1   ,     1.    ,\n",
       "          10.    ,   100.    ,  1000.    ]), 'clf__kernel': ['linear']}, {'clf__C': array([    0.0001,     0.001 ,     0.01  ,     0.1   ,     1.    ,\n",
       "          10.    ,   100.    ,  1000.    ]), 'clf__kernel': ['rbf'], 'clf__gamma': array([    0.0001,     0.001 ,     0.01  ,     0.1   ,     1.    ,\n",
       "          10.    ,   100.    ,  1000.    ])}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'clf__C': 0.01, 'clf__kernel': 'linear'} Best score: 0.9839\n"
     ]
    }
   ],
   "source": [
    "print(\"Best params: %s Best score: %.4f\" % (gs.best_params_, gs.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best param: {'max_depth': 3}, best score: 0.8790322580645161\n"
     ]
    }
   ],
   "source": [
    "# Applying Grid Search on Decision Tree\n",
    "tree = DecisionTreeClassifier(random_state = 0)\n",
    "param_grid = [\n",
    "    {\"max_depth\": [1, 2, 3, 4, 5, 6, 7, None]}\n",
    "]\n",
    "\n",
    "gs_tree = GridSearchCV(estimator = tree, param_grid = param_grid, scoring = \"accuracy\", cv = 5)\n",
    "gs_tree.fit(X_train, y_train)\n",
    "print(\"Best param: %s, best score: %s\" % (gs_tree.best_params_, gs_tree.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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